{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import librosa\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_PATH = '../../dataset/trainset/'\n",
    "labels = [ 'down', 'go', 'left', 'no', 'off', 'on', 'right', 'stop', 'up', 'yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_names = []\n",
    "\n",
    "path = TRAIN_PATH + labels[1] + '/'\n",
    "pathname = Path(path)\n",
    "\n",
    "for cp in pathname.iterdir():\n",
    "    term = Path(cp)\n",
    "    file_names.append(term.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mfcc(test_file):\n",
    "    y, sr = librosa.load(test_file,sr=None)\n",
    "    # hop_length means the samples' number\n",
    "#     hop_time = 20 # ms\n",
    "#     hop_length = np.round(hop_time*sr/1000).astype(int)\n",
    "    mfccs = librosa.feature.mfcc(y=y, sr=sr, hop_length=hop_length, n_mfcc=13)\n",
    "    return mfccs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_file = path + file_names[6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "y, sr = librosa.load(test_file,sr=None)\n",
    "# hop_length means the samples' number\n",
    "# y = np.pad(y,1024,mode='reflect')\n",
    "hop_time = 20 # ms\n",
    "hop_length = np.round(hop_time*sr/1000).astype(int)\n",
    "mfccs = librosa.feature.mfcc(y=y, sr=sr, hop_length=hop_length, n_mfcc=13, n_fft=2048)\n",
    "# , n_mels = 50,fmin=0, fmax=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mfccs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "# plt.plot(np.linspace(0, len(audio) / sample_rate, num=len(audio)), audio)\n",
    "plt.imshow(mfccs, aspect='auto', origin='lower');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_file = path + file_names[1]\n",
    "mfccs = get_mfcc(test_file)\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.imshow(mfccs, aspect='auto', origin='lower');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAEvCAYAAADB37lNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXAElEQVR4nO3dbaykZ30e8Os/Z86++CVdU6hlMCkkolQpiiBaRU0TpREkLUmjmEpRhCUqaCO5H5qWtJVSkn4grVQpatMolRoRuYGEqgQ3AtKgKG1xUyIaKXVYjBNsTIJLeTE12Mg4xuDdPefM3Q87GznWrpc9M8997p35/aTVnjNzdq575n7uZ55rZ84z1VoLAAAAR2t21AMAAABAOQMAABiCcgYAADAA5QwAAGAAyhkAAMAAlDMAAIABzHuGHT91ot1wy409I7mCTfogheqWNP2j1u++AFfSuq3IHnvk6e/LZj2vbM696bcdT8tz/SFyanPuy6Z47MHHv9Rae8Glrutazm645cb8zV9+Xc9IrmDRNmc5zTrsfC7kLCbP2O2QwdVZeOrZWnuLnS45PfYtizb9G2Y26XllPjs46iGszX6n7Xhqm/Rcf7zT9rXbIWde02f02H/18rbT7/rM5a7bnHsJAABwDVPOAAAABqCcAQAADEA5AwAAGIByBgAAMIArlrOqekdVPVpV9z/jsn9TVZ+oqj+sql+vqlOTjhIAAGDDfT2vnP1Kktc+67K7k7yitfatSf44yU+ueVwAAABb5YrlrLX2oSSPP+uyD7TW9pff/u8kt04wNgAAgK2xjt85+3tJ/uvlrqyqO6rqTFWdOfvE2TXEAQAAbJ6VyllV/fMk+0nedbmfaa3d2Vo73Vo7feLUiVXiAAAANtb8sP+wqt6U5AeTvKa11tY2IgAAgC10qHJWVa9N8hNJ/npr7WvrHRIAAMD2+XpOpf/uJL+X5OVV9XBV/WiSf5/kxiR3V9V9VfWLE48TAABgo13xlbPW2u2XuPjtE4wFAABga63jbI0AAACsSDkDAAAYgHIGAAAwAOUMAABgAMoZAADAAA79IdSH0ZIsWk2aMSufh3015rODLjm7teiS08Mi027DvTJmsVauRq/Hq8c+bOr9cJLstT7/97fokDPrtP/a6fL8Nf19mU2/eSVJjnd4/trtkPHk3onJM5I+zyvPOzb9R9/Oq89xS499S6/95N5iZ/KMp9vu5BnbwitnAAAAA1DOAAAABqCcAQAADEA5AwAAGIByBgAAMADlDAAAYADKGQAAwACUMwAAgAEoZwAAAANQzgAAAAagnAEAAAxAOQMAABiAcgYAADAA5QwAAGAAyhkAAMAAlDMAAIABKGcAAAADUM4AAAAGoJwBAAAMQDkDAAAYwLxnWKVlPjvoGTmZRdNrr8Zeh8drk+ZkVosOGW3yjE2ySPUJ2pBp2em2fU2/VnY7rMekz5qc9dqOO9jvsM/fP+jx3NVnTnrkfOnc9ZNn3Dg/N3lGko05Xk06HVN02L56HEuOYDvuJQAAwOCUMwAAgAEoZwAAAANQzgAAAAagnAEAAAzgiuWsqt5RVY9W1f3PuOx5VXV3VX1y+fdN0w4TAABgs309r5z9SpLXPuuytyT57dbay5L89vJ7AAAADumK5ay19qEkjz/r4tuSvHP59TuTvG69wwIAANguh/2ds5tba48sv/5CkpvXNB4AAICttPIJQVprLUm73PVVdUdVnamqM2efOLtqHAAAwEY6bDn7YlXdkiTLvx+93A+21u5srZ1urZ0+cerEIeMAAAA222HL2fuTvHH59RuT/MZ6hgMAALCdvp5T6b87ye8leXlVPVxVP5rkZ5J8X1V9Msn3Lr8HAADgkOZX+oHW2u2Xueo1ax4LAADA1lr5hCAAAACsTjkDAAAYgHIGAAAwAOUMAABgAMoZAADAAJQzAACAAVzxVPrrVEl2qk2acdBq0tu/aNEpZ2qLttMnJ5vxeCXJLNNuwxdTprY3ecIFU6/5pM+czGsxeUYv+21z/l9ut8O89Np/Pb2/O3nGJs39sdnB5Bknd6bfU5489vTkGUly7mD6Q76vHhybPOOxczdMnpH0Oc7rdSx5sJh+3ffYT/Y5/kqqw3HLc9mcvTQAAMA1TDkDAAAYgHIGAAAwAOUMAABgAMoZAADAAJQzAACAAShnAAAAA1DOAAAABqCcAQAADEA5AwAAGIByBgAAMADlDAAAYADKGQAAwACUMwAAgAEoZwAAAANQzgAAAAagnAEAAAxAOQMAABiAcgYAADAA5QwAAGAA896Bs7RpA2ram/9Ts4PJIxZtc7rzvKZ/vHZq4m2LQzlo0y/KvbYzeUYvs1pMntFjrfSY9yQ56LDT31/02b7Od8rZFF/b350844nzJyfP2F/0ea5vHdbk8fn+5BnXzc9PnpF0OF7dMLMOzyuLTs8rR21zjv4BAACuYcoZAADAAJQzAACAAShnAAAAA1DOAAAABrBSOauqf1xVD1TV/VX17qo6sa6BAQAAbJNDl7OqelGSf5TkdGvtFUl2krx+XQMDAADYJqu+rXGe5GRVzZNcl+T/rT4kAACA7XPoctZa+3ySn03y2SSPJPmT1toH1jUwAACAbbLK2xpvSnJbkpcmeWGS66vqDZf4uTuq6kxVnTn7xNnDjxQAAGCDrfK2xu9N8n9ba4+11vaSvC/JX3v2D7XW7mytnW6tnT5xyvlCAAAALmWVcvbZJH+1qq6rqkrymiQPrmdYAAAA22WV3zm7J8l7ktyb5GPL27pzTeMCAADYKvNV/nFr7a1J3rqmsQAAAGytVU+lDwAAwBooZwAAAANQzgAAAAagnAEAAAxAOQMAABiAcgYAADCAlU6lf7WqWuazg0kzFq1P35ylJs9YVJs846BNfz+SXvOy6JDB1drpsB0fn+1NnrHotFZ6WHTYf+3W5qzHG46d75Izy/RrpYdZp7mfddm37E+ecW7R51Csx335yv6JyTMeP3/d5BlJcn6x0yWnh/0O96XH88qm7COvxCtnAAAAA1DOAAAABqCcAQAADEA5AwAAGIByBgAAMADlDAAAYADKGQAAwACUMwAAgAEoZwAAAANQzgAAAAagnAEAAAxAOQMAABiAcgYAADAA5QwAAGAAyhkAAMAAlDMAAIABKGcAAAADUM4AAAAGoJwBAAAMQDkDAAAYwLxnWGuV/cXOpBmL1KS3f9HexPcjSfab7nw1Zq3P3M+qdchYTJ7Ry0GHeTlo06/HXhYd1n2P7Wuv05z02E+eW/R5qjw+2588o8f+q5dFh33Lw3snJs+Y+rjooh7HR/M6mDzjxM706yRJrpufnzxjp9N6nGX6nPls+rnv9Xj1OG55Lo7+AQAABqCcAQAADEA5AwAAGIByBgAAMADlDAAAYAArlbOqOlVV76mqT1TVg1X1HesaGAAAwDZZ9fzA/y7Jf2ut/XBVHUty3RrGBAAAsHUOXc6q6s8l+e4kb0qS1tr5JNN/KAQAAMAGWuVtjS9N8liSX66qj1bVL1XV9WsaFwAAwFZZpZzNk3xbkre11l6V5KtJ3vLsH6qqO6rqTFWdOfvE2RXiAAAANtcq5ezhJA+31u5Zfv+eXChrf0Zr7c7W2unW2ukTp06sEAcAALC5Dl3OWmtfSPK5qnr58qLXJPn4WkYFAACwZVY9W+M/TPKu5ZkaP5Xk764+JAAAgO2zUjlrrd2X5PR6hgIAALC9VvoQagAAANZDOQMAABiAcgYAADAA5QwAAGAAyhkAAMAAlDMAAIABrPo5Z1elqmU+O5g0Y7cWk97+RfPdae9HL4tWnXL8P8DV2OvwePWak1mHNblTbfKMXg46rMn9xc7kGbOJ9/UX7XbK6eHYbP+oh8Cz3Hz8yckz9tr06zFJnj44NnnGucX0h5X7G3Q80WN/n/SZl0WH7Wt/0Wfuex0bX87mbOEAAADXMOUMAABgAMoZAADAAJQzAACAAShnAAAAA1DOAAAABqCcAQAADEA5AwAAGIByBgAAMADlDAAAYADKGQAAwACUMwAAgAEoZwAAAANQzgAAAAagnAEAAAxAOQMAABiAcgYAADAA5QwAAGAAyhkAAMAA5r0Dd6pNevtPH+xOevsXnds7OXnGfpu+O88y7Xz0tEh1yenxmM1nB5Nn7NZi8owLevwf0PT3ZdFhPV7I6bMdT63X/Xhq/9jkGVM/b110cnZ+8oxZh/vSIyNJdmv6/eROh/3kE+evmzwjSfYWO5NnfPapmybPePL88ckzkuRgMf0+f/+gz/NK25DnlflOn+OWnVmv46NL88oZAADAAJQzAACAAShnAAAAA1DOAAAABqCcAQAADEA5AwAAGMDK5ayqdqrqo1X1m+sYEAAAwDZaxytnb07y4BpuBwAAYGutVM6q6tYkfyvJL61nOAAAANtp1VfOfj7JTyQ52o/SBgAAuMYdupxV1Q8mebS19pEr/NwdVXWmqs6c/fK5w8YBAABstFVeOfvOJD9UVZ9OcleSV1fVf3r2D7XW7mytnW6tnT5x0/EV4gAAADbXoctZa+0nW2u3ttZekuT1Sf5na+0NaxsZAADAFvE5ZwAAAAOYr+NGWmu/k+R31nFbAAAA28grZwAAAANQzgAAAAagnAEAAAxAOQMAABiAcgYAADCAtZyt8etVSeZ1MGnGfD7t7V90su1NnrFITZ4xS5s8I0lmteiS08N+25k8Y28xfcZGzUmHx6vHekz6rMnjO/uTZ/Tat5zskNFrrTy5P/29WbTpt+Nea+Wgw33p4Yb5+aMewtq84ORTk2d88zd8afKMZPrj1SRZtM15jaTXuu+hx77lw89x3eZsFQAAANcw5QwAAGAAyhkAAMAAlDMAAIABKGcAAAADUM4AAAAGoJwBAAAMQDkDAAAYgHIGAAAwAOUMAABgAMoZAADAAJQzAACAAShnAAAAA1DOAAAABqCcAQAADEA5AwAAGIByBgAAMADlDAAAYADKGQAAwACUMwAAgAHMj3oA16pZLaYPadN35y73I8msWpecHmZt+vuyOzuYPGOTHJ+fO+ohrM2iw7pfpDYiI0lmmX49PrF3cvKMJLn/sVsmz/ja2WOTZ7TWZ+6PHdufPOOv/IUvTJ7xyhsemjwjSRYd5uWz5/785Bmf+MrNk2ckyVf2TkyecW6/z2H4QYe577Hqj8+nX/PJ0R+DeeUMAABgAMoZAADAAJQzAACAAShnAAAAA1DOAAAABqCcAQAADODQ5ayqXlxVH6yqj1fVA1X15nUODAAAYJus8gEL+0n+aWvt3qq6MclHquru1trH1zQ2AACArXHoV85aa4+01u5dfv2VJA8medG6BgYAALBN1vI7Z1X1kiSvSnLPOm4PAABg26xczqrqhiTvTfLjrbUnL3H9HVV1pqrOPP3ls6vGAQAAbKSVyllV7eZCMXtXa+19l/qZ1tqdrbXTrbXTJ286sUocAADAxlrlbI2V5O1JHmyt/dz6hgQAALB9Vnnl7DuT/J0kr66q+5Z/fmBN4wIAANgqhz6Vfmvtd5PUGscCAACwtdZytkYAAABWo5wBAAAMQDkDAAAYgHIGAAAwAOUMAABgAIc+W+NhHJvt58UnHp80Y9H69M2zi90uOVPbqUWXnIMO87LodPLQ47U/ecaJ2d7kGbNOc7+TNnnGXtuZPOPAyWmvyt6iz9PLuTZ9zqndr02ekSR/6SWPTp6xWwcbkdHL4/vXT57x5Q4ZSXLz7p9MnvH6m35/8owXPr/P9rXTYZ9/vPrsJ3vk7NTmvN5z0KY/Pjr2HNdtziMJAABwDVPOAAAABqCcAQAADEA5AwAAGIByBgAAMADlDAAAYADKGQAAwACUMwAAgAEoZwAAAANQzgAAAAagnAEAAAxAOQMAABiAcgYAADAA5QwAAGAAyhkAAMAAlDMAAIABKGcAAAADUM4AAAAGoJwBAAAMQDkDAAAYwLxn2JMP7OR/vOLGSTNq99ikt/+nOcd2p8/Y2Zk8ox0cTJ5xIahNn1E1fUaStr8/fcbe9Blpi+kz0mc7To+MXjqsyR7bcGZ95mR24vjkGXXdyckzkuTgm144ecbeqekfrzbrsy+ePz39drzz1b3JM7o8PybZ/4bp5/4XXjp9xlPf2Gf7OveCDsdHN3TYvpLMdvs830+u9Zn7xUGPnJ+67DVeOQMAABiAcgYAADAA5QwAAGAAyhkAAMAAlDMAAIABKGcAAAADWKmcVdVrq+qPquqhqnrLugYFAACwbQ5dzqpqJ8kvJPn+JN+S5Paq+pZ1DQwAAGCbrPLK2bcneai19qnW2vkkdyW5bT3DAgAA2C6rlLMXJfncM75/eHnZn1FVd1TVmao6s5dzK8QBAABsrslPCNJau7O1drq1dno3x6eOAwAAuCatUs4+n+TFz/j+1uVlAAAAXKVVytmHk7ysql5aVceSvD7J+9czLAAAgO0yP+w/bK3tV9WPJfnvSXaSvKO19sDaRgYAALBFDl3OkqS19ltJfmtNYwEAANhak58QBAAAgCtTzgAAAAagnAEAAAxAOQMAABiAcgYAADCAaq31C6t6LMlnrvKfPT/JlyYYDmMz79vL3G8vc7+9zP32Mvfba5vn/i+21l5wqSu6lrPDqKozrbXTRz0O+jLv28vcby9zv73M/fYy99vL3F+atzUCAAAMQDkDAAAYwLVQzu486gFwJMz79jL328vcby9zv73M/fYy95cw/O+cAQAAbINr4ZUzAACAjTdsOauq11bVH1XVQ1X1lqMeD/1U1aer6mNVdV9VnTnq8TCdqnpHVT1aVfc/47LnVdXdVfXJ5d83HeUYmcZl5v6nq+rzy7V/X1X9wFGOkfWrqhdX1Qer6uNV9UBVvXl5uXW/4Z5j7q37DVdVJ6rq96vqD5Zz/y+Wl7+0qu5ZHuv/56o6dtRjHcGQb2usqp0kf5zk+5I8nOTDSW5vrX38SAdGF1X16SSnW2vb+tkXW6OqvjvJU0n+Y2vtFcvL/nWSx1trP7P8j5mbWmv/7CjHyfpdZu5/OslTrbWfPcqxMZ2quiXJLa21e6vqxiQfSfK6JG+Kdb/RnmPufyTW/UarqkpyfWvtqaraTfK7Sd6c5J8keV9r7a6q+sUkf9Bae9tRjnUEo75y9u1JHmqtfaq1dj7JXUluO+IxAWvWWvtQksefdfFtSd65/PqdufDkzYa5zNyz4Vprj7TW7l1+/ZUkDyZ5Uaz7jfccc8+Gaxc8tfx2d/mnJXl1kvcsL7ful0YtZy9K8rlnfP9wLOBt0pJ8oKo+UlV3HPVg6O7m1tojy6+/kOTmoxwM3f1YVf3h8m2P3tq2warqJUleleSeWPdb5Vlzn1j3G6+qdqrqviSPJrk7yf9J8kRrbX/5I471l0YtZ2y372qtfVuS70/yD5Zvf2ILtQvvux7vvddM5W1JvjnJK5M8kuTfHulomExV3ZDkvUl+vLX25DOvs+432yXm3rrfAq21g9baK5PcmgvvkPvLRzuicY1azj6f5MXP+P7W5WVsgdba55d/P5rk13NhEbM9vrj83YSLv6Pw6BGPh05aa19cPoEvkvyHWPsbafk7J+9N8q7W2vuWF1v3W+BSc2/db5fW2hNJPpjkO5Kcqqr58irH+kujlrMPJ3nZ8iwux5K8Psn7j3hMdFBV1y9/UThVdX2Sv5Hk/uf+V2yY9yd54/LrNyb5jSMcCx1dPDhf+tux9jfO8sQAb0/yYGvt555xlXW/4S4399b95quqF1TVqeXXJ3PhhH8P5kJJ++Hlj1n3S0OerTFJlqdS/fkkO0ne0Vr7V0c7Inqoqm/KhVfLkmSe5FfN/eaqqncn+Z4kz0/yxSRvTfJfkvxakm9M8pkkP9Jac+KIDXOZuf+eXHhrU0vy6SR//xm/h8QGqKrvSvK/knwsyWJ58U/lwu8eWfcb7Dnm/vZY9xutqr41F074sZMLLwz9WmvtXy6P+e5K8rwkH03yhtbauaMb6RiGLWcAAADbZNS3NQIAAGwV5QwAAGAAyhkAAMAAlDMAAIABKGcAAAADUM4AAAAGoJwBAAAMQDkDAAAYwP8HY+4u9sGI5ucAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_file = path + file_names[2]\n",
    "mfccs = get_mfcc(test_file)\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.imshow(mfccs, aspect='auto', origin='lower');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mfccs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PY38",
   "language": "python",
   "name": "py38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
